Knowledge that Transforms

We analyze the dynamic strategic interactions between a manufacturer and a retailer in a decentralized distribution channel used to launch an innovative durable product (IDP). The underlying retail demand for the IDP is influenced by word‐of‐mouth from past adopters and follows a Bass‐type diffusion process. The word‐of‐mouth influence creates a trade‐off between immediate and future sales and profits, resulting in a multi‐period dynamic supply chain coordination problem. Our analysis shows that while in some environments, the manufacturer is better off with a far‐sighted retailer, there are also environments in which the manufacturer is better off with a myopic retailer. We characterize equilibrium dynamic pricing strategies and the resulting sales and profit trajectories. We demonstrate that revenue‐sharing contracts can coordinate the IDP's supply chain with both far‐sighted and myopic retailers throughout the entire planning horizon and arbitrarily allocate the channel profit.

We study the joint decisions of offering mail‐in rebates (MIRs) in a single‐manufacturer–single‐retailer supply chain using a game theoretic framework. Either party can offer an MIR to the end consumer if it is in his best interest. The consumer demand is stochastic and depends on the product price and the amount of MIRs. When the retail price is exogenous, we show the existence of a unique Nash equilibrium under both additive and multiplicative demand functions and characterize it completely. We show that any of the following four scenarios can be the equilibrium: both parties offer MIR, only one party offers MIR, none offers MIR. When the retail price is a decision variable for the retailer and the rebate redemption rate increases with the amount of MIR, we once again prove the existence of a unique Nash equilibrium where both the retailer and the manufacturer offer MIRs. Using a numerical study, we show that the average post‐purchase price of the product is higher not only than the perceived pre‐purchase price but also than the newsvendor optimal price without an MIR. This implies that an MIR makes a product look cheaper while the consumers actually pay more on average.

Consider a buyer, facing uncertain demand, who sources from multiple suppliers via online procurement auctions (open descending price‐only auctions). The suppliers have heterogeneous production costs, which are private information, and the winning supplier has to invest in production capacity before the demand uncertainty is resolved. The buyer chooses to offer a push or pull contract, for which the single price and winning supplier are determined via the auction. We show that, with a pull contract, the buyer does not necessarily benefit from a larger number of suppliers participating in the auction, due to the negative effect of supplier competition on the incentive of supplier capacity investment. We thus propose an enhanced pull mechanism that mitigates this effect with a floor price. We then analyze and compare the outcomes of auctions for push and (enhanced) pull contracts, establishing when one form is preferred over the other based on the buyer's profits. We also compare our simple, price‐only push and pull contract auctions to the optimal mechanisms, benchmarking the performance of the simple mechanisms as well as establishing the relative importance of auction design and contract design in procurement auctions.

Two factors that their influence on the demand has been investigated in many papers are (i) the shelf space allocated to a product and to its complement or supplement products and (ii) the instantaneous inventory level seen by customers. Here we analyze the joint shelf space allocation and inventory decisions for multiple items with demand that depends on both factors. The traditional approach to solve inventory models with a state‐dependent demand rate uses a time domain approach. However, this approach often does not lead to closed‐form expressions for the profit rate with both dependencies. We analyze the problem in the inventory domain via level crossing theory. This approach leads to closed‐form expressions for a large set of demand rate functions exhibiting both dependencies. These closed‐form expressions substantially simplify the search for optimal solutions; thus we use them to solve the joint inventory control and shelf space allocation problem. We consider examples with two products to investigate the significance of capturing both demand dependencies. We show that in some settings it is important to capture both dependencies. We consider two heuristics, each one of them ignores one of the two dependencies. Using these heuristics it seems that ignoring the dependency on the shelf space might be less harmful than ignoring the dependency on the inventory level, which, based on computational results, can lead to profit losses of more than 6%. We demonstrate that retailers should use their operational control, e.g., reorder point, to promote higher demand products.

This paper investigates the effect of learning and forgetting on production scheduling decisions. Numerous papers have appeared on this topic in the last four decades; they show that firms are better off producing in larger batches in the presence of learning and forgetting. However, these papers fail to consider one or more of realistic features of learning and forgetting; factors such as (1) the amount forgotten increases with break length between two batches and (2) the forgetting could be slow over an initial short interval followed by fast forgetting. Our paper contributes by demonstrating that a consideration of these realistic features leads to a different conclusion—firms may be better off producing in smaller batches in the presence of learning and forgetting. This is a new insight that provides one more justification for producing in small batches.

India is an important frontier for economic growth, investments, and development. The service sector, like the manufacturing industry, in India is booming. Following the trend of their western counterparts, service organizations in India are implementing enterprise‐level information and communication technologies (ICTs) to support service processes. In this paper, we used socio‐technical systems theory to develop hypotheses about the effects of ICTs on the five job characteristics, i.e., skill variety, task identity, task significance, autonomy, and feedback, in the job characteristics model (JCM) in a service organization (a bank) in India. We also tested the entire JCM that relates job characteristics to job satisfaction and job performance via various mediators and moderators. In a 32‐month longitudinal field study of 1743 employees, we gathered one wave of data before an ICT implementation and two waves after the implementation. We found that, although the ICT enriched employees' job characteristics, employees reported significantly lower job satisfaction and job performance. To understand this puzzling finding, we conducted a qualitative study and identified four contextual forces that contribute to these results and hinder successful implementation of ICTs in the service sector in India and, possibly, other developing countries: environmental barriers, learning difficulty, culture shock, and employee valuation.

Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real‐time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base‐stock policy, in which the optimal base‐stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.

Under group buying, quantity discounts are offered based on the buyers' aggregated purchasing quantity, instead of individual quantities. As the price decreases with the total quantity, buyers receive lower prices than they otherwise would be able to obtain individually. Previous studies on group buying focus on the benefit buyers receive in reduced acquisition costs or enhanced bargaining power. In this paper, we show that buyers can instead get hurt from such cooperation. Specifically, we consider a two‐level distribution channel with a single manufacturer and two retailers who compete for end customers. We show that, under linear demand curves, group buying is always preferable for symmetric (i.e., identical) retailers. For asymmetric retailers (i.e., differing in market base and/or efficiency), group buying is beneficial to the smaller (or less efficient) player. However, it can be detrimental to the larger (or more efficient) one. Despite the lower wholesale price under group buying, the manufacturer can receive a higher revenue. Interestingly, group buying is more likely to form when retailers are competitive in different dimensions. These insights are shown to be robust under general nonlinear demand curves, except for constant elastic demand with low demand elasticity.

ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.

This paper examines the choice of supply chain structure for a proprietary component manufacturer (PCM). The PCM, who is the sole supply source of a critical component used to assemble an end product, can either provide its component to an original equipment manufacturer (OEM) in the end‐product market (component supplier structure), develop the end product exclusively under its own brand (monopoly structure), or provide the component to the OEM as well as develop the end product under its own brand (dual distributor structure). Typically, the end products of the PCM and the OEM will be differentiated, and the OEM tends to have a capability advantage (compared with the PCM) in producing the end product. Our paper studies the impact of this degree of differentiation and capability advantage on the optimal choice of distribution structure. We then investigate how investing in component branding, enhancing the value of the end product, using alternative supply contracts, and product valuation uncertainty influence the PCM's optimal choice of distribution structure.